This code should take all of the answers and store them in a big list.
answers_list = list()
index = 0
for(this_prior in c(TRUE, FALSE)){
for(this_diffuseness in c(1, 2, 4, 8)){
file_name = paste0("answers_informative=", this_prior, "_diffuseness=", this_diffuseness,".RData")
load(file_name)
answers_list[[file_name]] = answers$coda_answers
}
}
From this list, we’re interested in estimate bias, ESS and \(\hat{r}\) across conditions. This next code chunk will return the 5 worst performing parameters for each condition.
get_statistics = function(list){
abs_bias = abs(list$true_values[1:27] - list$mean[1:27])
ess = list$ESS[1:27]
rhat = list$RHAT[1:27]
names(abs_bias) = rownames(list[1:27,])
names(ess) = rownames(list[1:27,])
names(rhat) = rownames(list[1:27,])
return(list(absolute_bias = sort(abs_bias, decreasing = T)[1:5],
expected_sample_size = sort(ess)[1:5],
r_hat = sort(rhat, decreasing = T)[1:5]))
}
lapply(answers_list, get_statistics)
## $`answers_informative=TRUE_diffuseness=1.RData`
## $`answers_informative=TRUE_diffuseness=1.RData`$absolute_bias
## direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,5] direct_effect[1,1]
## 0.7724 0.4122 0.3634 0.3121
## X_fixed_effect[4,1]
## 0.2901
##
## $`answers_informative=TRUE_diffuseness=1.RData`$expected_sample_size
## direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,4] direct_effect[1,1]
## 282 283 1020 1027
## M_fixed_effect[1,2]
## 1102
##
## $`answers_informative=TRUE_diffuseness=1.RData`$r_hat
## M_fixed_effect[1,4] direct_effect[1,1] direct_effect[1,2] X_fixed_effect[5,2]
## 1.0036 1.0024 1.0019 1.0014
## M_fixed_effect[1,1]
## 1.0013
##
##
## $`answers_informative=TRUE_diffuseness=2.RData`
## $`answers_informative=TRUE_diffuseness=2.RData`$absolute_bias
## direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,5] direct_effect[1,1]
## 0.7740 0.4117 0.3325 0.3050
## X_fixed_effect[4,1]
## 0.2897
##
## $`answers_informative=TRUE_diffuseness=2.RData`$expected_sample_size
## direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,4] M_fixed_effect[1,3]
## 234 235 942 946
## direct_effect[1,1]
## 971
##
## $`answers_informative=TRUE_diffuseness=2.RData`$r_hat
## direct_effect[1,3] M_fixed_effect[1,1] direct_effect[1,1] direct_effect[1,2]
## 1.0096 1.0094 1.0046 1.0026
## M_fixed_effect[1,6]
## 1.0015
##
##
## $`answers_informative=TRUE_diffuseness=4.RData`
## $`answers_informative=TRUE_diffuseness=4.RData`$absolute_bias
## direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,5] M_fixed_effect[1,6]
## 2.9352 1.4661 0.5405 0.4164
## X_fixed_effect[4,1]
## 0.2954
##
## $`answers_informative=TRUE_diffuseness=4.RData`$expected_sample_size
## X_fixed_effect[1,3] X_fixed_effect[2,3] X_fixed_effect[2,2] X_fixed_effect[1,2]
## 13 21 45 53
## M_fixed_effect[1,5]
## 55
##
## $`answers_informative=TRUE_diffuseness=4.RData`$r_hat
## X_fixed_effect[1,3] X_fixed_effect[2,3] M_fixed_effect[1,2] X_fixed_effect[2,2]
## 3.1295 2.7646 2.2885 2.2016
## M_fixed_effect[1,5]
## 2.0519
##
##
## $`answers_informative=TRUE_diffuseness=8.RData`
## $`answers_informative=TRUE_diffuseness=8.RData`$absolute_bias
## direct_effect[1,3] X_fixed_effect[1,2] M_fixed_effect[1,1] X_fixed_effect[2,1]
## 3.8901 2.9844 1.8617 1.1206
## X_fixed_effect[3,3]
## 1.1126
##
## $`answers_informative=TRUE_diffuseness=8.RData`$expected_sample_size
## X_fixed_effect[1,2] X_fixed_effect[5,3] X_fixed_effect[6,3] X_fixed_effect[2,1]
## 1 1 2 3
## X_fixed_effect[2,3]
## 3
##
## $`answers_informative=TRUE_diffuseness=8.RData`$r_hat
## X_fixed_effect[5,3] X_fixed_effect[1,2] X_fixed_effect[6,3] X_fixed_effect[2,3]
## 20.2772 17.7096 16.3637 11.3524
## X_fixed_effect[2,1]
## 11.0613
##
##
## $`answers_informative=FALSE_diffuseness=1.RData`
## $`answers_informative=FALSE_diffuseness=1.RData`$absolute_bias
## direct_effect[1,3] M_fixed_effect[1,1] direct_effect[1,1] X_fixed_effect[4,1]
## 1.1790 0.6038 0.3471 0.2893
## X_fixed_effect[1,1]
## 0.1057
##
## $`answers_informative=FALSE_diffuseness=1.RData`$expected_sample_size
## M_fixed_effect[1,1] direct_effect[1,3] direct_effect[1,1] M_fixed_effect[1,3]
## 258 264 844 927
## M_fixed_effect[1,4]
## 968
##
## $`answers_informative=FALSE_diffuseness=1.RData`$r_hat
## direct_effect[1,2] M_fixed_effect[1,5] direct_effect[1,1] M_fixed_effect[1,1]
## 1.0027 1.0023 1.0020 1.0013
## direct_effect[1,3]
## 1.0013
##
##
## $`answers_informative=FALSE_diffuseness=2.RData`
## $`answers_informative=FALSE_diffuseness=2.RData`$absolute_bias
## direct_effect[1,3] M_fixed_effect[1,1] direct_effect[1,1] X_fixed_effect[4,1]
## 1.1489 0.5806 0.3058 0.2953
## M_fixed_effect[1,5]
## 0.1929
##
## $`answers_informative=FALSE_diffuseness=2.RData`$expected_sample_size
## X_fixed_effect[4,3] M_fixed_effect[1,1] direct_effect[1,3] X_fixed_effect[4,2]
## 107 166 177 242
## M_fixed_effect[1,4]
## 403
##
## $`answers_informative=FALSE_diffuseness=2.RData`$r_hat
## X_fixed_effect[4,3] X_fixed_effect[4,2] M_fixed_effect[1,4] X_fixed_effect[3,3]
## 1.3768 1.1906 1.1725 1.0996
## direct_effect[1,1]
## 1.0929
##
##
## $`answers_informative=FALSE_diffuseness=4.RData`
## $`answers_informative=FALSE_diffuseness=4.RData`$absolute_bias
## M_fixed_effect[1,2] M_fixed_effect[1,3] direct_effect[1,3] M_fixed_effect[1,4]
## 0.7392 0.5406 0.5386 0.4916
## M_fixed_effect[1,6]
## 0.4880
##
## $`answers_informative=FALSE_diffuseness=4.RData`$expected_sample_size
## M_fixed_effect[1,2] X_fixed_effect[2,2] M_fixed_effect[1,1] direct_effect[1,3]
## 25 44 54 58
## M_fixed_effect[1,3]
## 61
##
## $`answers_informative=FALSE_diffuseness=4.RData`$r_hat
## M_fixed_effect[1,2] X_fixed_effect[2,2] M_fixed_effect[1,1] direct_effect[1,3]
## 3.2397 2.2387 1.9778 1.8894
## M_fixed_effect[1,4]
## 1.7316
##
##
## $`answers_informative=FALSE_diffuseness=8.RData`
## $`answers_informative=FALSE_diffuseness=8.RData`$absolute_bias
## direct_effect[1,3] M_fixed_effect[1,1] X_fixed_effect[1,2] X_fixed_effect[5,3]
## 5.6021 2.8615 2.5455 1.3166
## X_fixed_effect[1,1]
## 1.1005
##
## $`answers_informative=FALSE_diffuseness=8.RData`$expected_sample_size
## X_fixed_effect[5,3] X_fixed_effect[1,2] X_fixed_effect[2,1] X_fixed_effect[1,3]
## 1 2 3 3
## X_fixed_effect[3,3]
## 3
##
## $`answers_informative=FALSE_diffuseness=8.RData`$r_hat
## X_fixed_effect[5,3] X_fixed_effect[3,3] X_fixed_effect[1,2] X_fixed_effect[1,3]
## 15.5195 7.3310 6.7433 5.5906
## X_fixed_effect[2,1]
## 5.1513
It seems the less diffuse options work much better than the more diffuse options. Lets go with uninformative and diffuseness = 1
Let’s see a plot of the chains for the conditions with mean 0 priors and less diffuseness
library(rjags)
## Loading required package: coda
## Linked to JAGS 4.3.1
## Loaded modules: basemod,bugs
load("answers_informative=FALSE_diffuseness=1.RData")
plot(answers$codaSamles) #there's a typo coded in my entry here, but the code should still work as expected